1 citations · 1 across the 10 of their papers we have counts for
12 papers
No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation
Seonghyeon Kim, Chaeyun Jang, Noah Lee +2
Multi-teacher on-policy distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on student-generated samples. We…
The Distributional View of Knowledge Distillation
Gordei Verbii, Juho Lee
Token-level knowledge distillation (KD) matches two conditional distributions per position, yet the standard objectives compare them pointwise: a Kullback-Leibler gradient is blind…
From Drift to Coherence: Stabilizing Beliefs in LLMs
SongEun Kim, Seungyoo Lee, Edwin Fong +2
Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been s…
Parallel Test-Time Scaling with Multi-Sequence Verifiers
Yegon Kim, Seungyoo Lee, Chaeyun Jang +2
Parallel test-time scaling, which generates multiple candidate solutions for a single problem, is a powerful technique for improving large language model performance. However, it i…
Mitigating Legibility Tax with Decoupled Prover-Verifier Games
Yegon Kim, Juho Lee
As large language models become increasingly capable, it is critical that their outputs can be easily checked by less capable systems. Prover-verifier games can be used to improve…
A Model-Free Universal AI
Yegon Kim, Juho Lee
In general reinforcement learning, all established optimal agents, including AIXI, are model-based, explicitly maintaining and using environment models. This paper introduces Unive…
Compact Memory for Continual Logistic Regression
Yohan Jung, Hyungi Lee, Wenlong Chen +4
Despite recent progress, continual learning still does not match the performance of batch training. To avoid catastrophic forgetting, we need to build compact memory of essential p…
Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision
Chaeyun Jang, Moonseok Choi, Yegon Kim +2
Uncertainty calibration is essential for the safe deployment of large language models (LLMs), particularly when users rely on verbalized confidence estimates. While prior work has…
Variational Bayesian Pseudo-Coreset
Hyungi Lee, Seungyoo Lee, Juho Lee
The success of deep learning requires large datasets and extensive training, which can create significant computational challenges. To address these challenges, pseudo-coresets, sm…
Dimension Agnostic Neural Processes
Hyungi Lee, Chaeyun Jang, Dongbok Lee +1
Meta-learning aims to train models that can generalize to new tasks with limited labeled data by extracting shared features across diverse task datasets. Additionally, it accounts…
Reliable Decision Making via Calibration Oriented Retrieval Augmented Generation
Chaeyun Jang, Deukhwan Cho, Seanie Lee +2
Recently, Large Language Models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confi…
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series
Byoungwoo Park, Hyungi Lee, Juho Lee
Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we…